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New methods enable adaptive image and video tokenization

By PulseAugur Editorial · [4 sources] ·

Researchers have developed new methods for adaptive image and video tokenization, allowing models to dynamically allocate computational resources based on visual complexity. AdaTok, a self-budgeting discrete 1D tokenizer, learns to adjust its token count per image, achieving competitive fidelity with significantly fewer tokens on average. Separately, a new framework for adaptive video tokenization uses temporal redundancy masking and latent inpainting to achieve efficient, content-driven token allocation, resulting in substantial inference-time speedups. AI

IMPACT These adaptive tokenization techniques could lead to more efficient AI models for image and video processing, reducing computational costs and increasing inference speeds.

RANK_REASON The cluster contains two distinct research papers introducing novel methods for adaptive tokenization in computer vision tasks.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

COVERAGE [4]

  1. arXiv cs.CV TIER_1 English(EN) · Xiaocheng Lu, Yuxi Chen, Jie Zhang, Jian Liu, Jingcai Guo, Fangqi Zhu, Tao Han, Song Guo ·

    AdaTok: Self-Budgeting Image Tokenization with Quality-Preserving Dynamic Tokens

    arXiv:2606.07185v1 Announce Type: new Abstract: Image tokenizers, from 2D grids to recent 1D sequences, typically encode every image with the same fixed number of tokens. Yet visual complexity is highly heterogeneous, so a uniform budget overspends on simple inputs and underserve…

  2. arXiv cs.CV TIER_1 English(EN) · Song Guo ·

    AdaTok: Self-Budgeting Image Tokenization with Quality-Preserving Dynamic Tokens

    Image tokenizers, from 2D grids to recent 1D sequences, typically encode every image with the same fixed number of tokens. Yet visual complexity is highly heterogeneous, so a uniform budget overspends on simple inputs and underserves complex ones. Existing elastic tokenizers expo…

  3. arXiv cs.CV TIER_1 English(EN) · Kevin Dave, Sai Aditya Patkuri, Chhaya Kumar Das, Gouranga Bala, R. Venkatesh Babu, Rajeshkumar SA ·

    Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting

    arXiv:2606.06158v1 Announce Type: new Abstract: Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous-regime approaches achieve this via iterative binarised searches or trained neural re…

  4. arXiv cs.CV TIER_1 English(EN) · Rajeshkumar SA ·

    Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting

    Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous-regime approaches achieve this via iterative binarised searches or trained neural regressors, while discrete methods often require a…

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